Fits complex parametric models using the method proposed by Cox and Kartsonaki (2012) without likelihoods.

All data in the book "Statistical Methods in Biology" by Welham et al. (2015) <doi:10.1201/b17336> with a corresponding documentation and illustrative analysis of the data.

This package performs simulations of binary spatial raster data using the Ising model (Ising (1925) <doi:10.1007/BF02980577>; Onsager (1944) <doi:10.1103/PhysRev.65.117>). It allows to set a few parameters that represent internal and external pressures, and the number of simulations (Stepinski and Nowosad (2023) <doi:10.1098/rsos.231005>).

Models high-dimensional data, such as RNA-seq or proteomic data using an item-by-item strategy. The package contains functions to wrap high-dimensional data and iterate over them using established R packages for regression modelling (e.g., glmmTMB or mgcv').

Determines Optimum Strata Boundaries (OSB) and Optimum Sample Sizes (OSS) for univariate stratified sampling designs under Neyman allocation. The stratification variable is described by a best-fitting parametric distribution, selected automatically by AIC from a set of continuous families (normal, log-normal, gamma, Weibull, exponential, Cauchy, uniform, Pareto, triangular and right-triangular), and the optimum boundaries are obtained by minimising the Neyman objective. Version 2.0 keeps the original globally optimal Dynamic Programming (DP) solver of Reddy and Khan (2020) as the default and adds two faster derivative-free alternatives for interactive and large-scale use: a multi-start COBYLA solver and a two-phase global solver that couples DIRECT-L with COBYLA refinement. It also provides cost-constrained allocation with unequal per-stratum costs, a design-efficiency comparison (compare_designs), two- and three-dimensional and interactive visualisations, solution-quality diagnostics (a Cauchy-Schwarz optimality gap and KKT first-order residuals for the derivative-free solvers) and a self-contained shiny application, while remaining backward compatible with the strata.data() and strata.distr() interface of version 1.x. The methodology follows Khan et al. (2008) <https://www150.statcan.gc.ca/n1/pub/12-001-x/2008002/article/10761-eng.pdf>, Reddy and Khan (2018) <doi:10.1111/anzs.12244> and Reddy and Khan (2020) <doi:10.1111/anzs.12301>.

In practice, it is difficult to determine the number of decomposition modes, K, for Variational Mode Decomposition (VMD). To overcome this issue, this study offers Spearman Variational Mode Decomposition (SVMD), a method that uses the Spearman correlation coefficient to calculate the ideal mode number. Unlike the Pearson correlation coefficient, which only returns a perfect value when X and Y are linearly connected, the Spearman correlation can be calculated without knowing the probability distributions of X and Y. The Spearman correlation coefficient, also called Spearman's rank correlation coefficient, is a subset of a wider correlation coefficient. As VMD decomposes a signal, the Spearman correlation coefficient between the reconstructed and original sequences rises as the mode number K increases. Once the signal has been fully decomposed, subsequent increases in K cause the correlation to gradually level off. When the correlation reaches a specific level, VMD is said to have adequately decomposed the signal. Numerous experiments revealed that a threshold of 0.997 produces the best denoising effect, so the threshold is set at 0.997. This package has been developed using concept of Yang et al. (2021)<doi:10.1016/j.aej.2021.01.055>.

Identify 17 Sustainable Development Goals and associated 169 targets in text.

Machine learning is widely used in information-systems design. Yet, training algorithms on imbalanced datasets may severely affect performance on unseen data. For example, in some cases in healthcare, financial, or internet-security contexts, certain sub-classes are difficult to learn because they are underrepresented in training data. This R package offers a flexible and efficient solution based on a new synthetic average neighborhood sampling algorithm ('SANSA'), which, in contrast to other solutions, introduces a novel â placementâ parameter that can be tuned to adapt to each datasets unique manifestation of the imbalance. More information about the algorithm's parameters can be found at Nasir et al. (2022) <https://murtaza.cc/SANSA/>.

An assortment of helper functions for doing structural equation modeling, mainly by lavaan for now. Most of them are time-saving functions for common tasks in doing structural equation modeling and reading the output. This package is not for functions that implement advanced statistical procedures. It is a light-weight package for simple functions that do simple tasks conveniently, with as few dependencies as possible.

Message translation is often managed with po files and the gettext programme, but sometimes another solution is needed. In contrast to po files, a more flexible approach is used as in the Fluent <https://projectfluent.org/> project with R Markdown snippets. The key-value approach allows easier handling of the translated messages.

This package provides an interface to search, read, query, and retrieve metadata for datasets hosted on Socrata open data portals. Supports all Socrata data types, including spatial data returned as sf objects.

This package provides a set of functions to quantify and visualise social autocorrelation.

Estimates cohort-by-time average treatment effects under staggered treatment adoption by the generalized method of moments. Three weighting schemes are provided, corresponding to a pooled stationary covariance, a cohort-specific stationary covariance, and an unrestricted within-cohort covariance. Optional adjustment for baseline covariates by outcome regression, and a serial-correlation robust over-identification test of parallel trends and no anticipation, are also supported. The methods are described in Arora and Bijani (2026) <doi:10.2139/ssrn.6558759>.

Create and format tables and APA statistics for scientific publication. This includes making a Table 1 to summarize demographics across groups, correlation tables with significance indicated by stars, and extracting formatted statistical summarizes from simple tests for in-text notation. The package also includes functions for Winsorizing data based on a Z-statistic cutoff.

This package provides functions to calculate some point estimators and estimate their variance under unequal probability sampling without replacement. Single and two-stage sampling designs are considered. Some approximations for the second-order inclusion probabilities (joint inclusion probabilities) are available (sample and population based). A variety of Jackknife variance estimators are implemented. Almost every function is written in C (compiled) code for faster results. The functions incorporate some performance improvements for faster results with large datasets.

Fitting a smooth path to a given set of noisy spherical data observed at known time points. It implements a piecewise geodesic curve fitting method on the unit sphere based on a velocity-based penalization scheme. The proposed approach is implemented using the Riemannian block coordinate descent algorithm. To understand the method and algorithm, one can refer to Bak, K. Y., Shin, J. K., & Koo, J. Y. (2023) <doi:10.1080/02664763.2022.2054962> for the case of order 1. Additionally, this package includes various functions necessary for handling spherical data.

Deals with Young tableaux (field of combinatorics). For standard Young tabeaux, performs enumeration, counting, random generation, the Robinson-Schensted correspondence, and conversion to and from paths on the Young lattice. Also performs enumeration and counting of semistandard Young tableaux, enumeration of skew semistandard Young tableaux, enumeration of Gelfand-Tsetlin patterns, and computation of Kostka numbers.

Researchers have been using simulated data from a multivariate linear model to compare and evaluate different methods, ideas and models. Additionally, teachers and educators have been using a simulation tool to demonstrate and teach various statistical and machine learning concepts. This package helps users to simulate linear model data with a wide range of properties by tuning few parameters such as relevant latent components. In addition, a shiny app as an RStudio gadget gives users a simple interface for using the simulation function. See more on: Sæbø, S., Almøy, T., Helland, I.S. (2015) <doi:10.1016/j.chemolab.2015.05.012> and Rimal, R., Almøy, T., Sæbø, S. (2018) <doi:10.1016/j.chemolab.2018.02.009>.

Simple result caching in R based on R.cache. The global environment is not considered when caching results simplifying moving files between multiple instances of R. Relies on more base functions than R.cache (e.g. cached results are saved using saveRDS() and readRDS()).

Collection (syllogi in greek) of real and fictitious data sets for teaching purposes. The datasets were manually entered by the author from the respective references as listed in the individual dataset documentation. The fictions datasets are the creation of the author, that he has found useful for teaching statistics.

Empirical likelihood methods for asymptotically efficient estimation of models based on conditional or unconditional moment restrictions; see Kitamura, Tripathi & Ahn (2004) <doi:10.1111/j.1468-0262.2004.00550.x> and Owen (2013) <doi:10.1002/cjs.11183>. Kernel-based non-parametric methods for density/regression estimation and numerical routines for empirical likelihood maximisation are implemented in Rcpp for speed.

This package provides tools for accessing and processing datasets prepared by the Foundation SmarterPoland.pl. Among all: access to API of Google Maps, Central Statistical Office of Poland, MojePanstwo, Eurostat, WHO and other sources.

Fits, spatially predicts and temporally forecasts large amounts of space-time data using [1] Bayesian Gaussian Process (GP) Models, [2] Bayesian Auto-Regressive (AR) Models, and [3] Bayesian Gaussian Predictive Processes (GPP) based AR Models for spatio-temporal big-n problems. Bakar and Sahu (2015) <doi:10.18637/jss.v063.i15>.

Analyzing soil food webs or any food web measured at equilibrium. The package calculates carbon and nitrogen fluxes and stability properties using methods described by Hunt et al. (1987) <doi:10.1007/BF00260580>, de Ruiter et al. (1995) <doi:10.1126/science.269.5228.1257>, Holtkamp et al. (2011) <doi:10.1016/j.soilbio.2010.10.004>, and Buchkowski and Lindo (2021) <doi:10.1111/1365-2435.13706>. The package can also manipulate the structure of the food web as well as simulate food webs away from equilibrium and run decomposition experiments.